Analyzing user satisfaction in e-learning platforms: A text mining and explainable machine learning approach using unstructured data

doi:10.1016/j.asoc.2026.115450

Analyzing user satisfaction in e-learning platforms: A text mining and explainable machine learning approach using unstructured data

2026-05-12

0

PRE

AI

Patrick Kwabena Mensah

K

Kwame Opuni-Boachie Obour Agyekum

S

Seth Larweh Kodjiku

E

Esther Stacy E. B. Aggrey

DOI：10.1016/j.asoc.2026.115450

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Abstract

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• Analyzed unstructured user reviews for rich insights into user experiences. • Utilized BERT-based sentiment analysis to uncover key user satisfaction factors. • Identified 13 topics from user reviews to enhance understanding of user feedback. • Evaluated 7 ML models, with Decision Tree excelling in predictive performance. • Combined SHAP and ANOVA for robust insights into user satisfaction factors.

Keywords:

user satisfaction

text mining

machine learning

BERT

SHAP

Analyzing user satisfaction in e-learning platforms: A text mining and explainable machine learning approach using unstructured data

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